{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 快速运行几个demo \n",
    "---\n",
    "#### 使用backtrader进行回测分析包含以下几个步骤：\n",
    "    - 创建策略\n",
    "        - 确定要调优的参数：可以分析确定是否有效的，或者通过回测要确定最优值的\n",
    "        - 初始化策略中用到的指标数据：通过引擎传入的数据实现\n",
    "        - 判断是否达到了买卖点\n",
    "    - 执行引擎\n",
    "        - 初始化引擎\n",
    "        - 注入创建的策略\n",
    "        - 加载和注入数据Feed\n",
    "        - 运行引擎\n",
    "        - 进行可视化分析\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.1 先运行引擎看看\n",
    "第一步是让程序跑起来，而不是考虑策略，因此首先初始化引擎运行看看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 10000.00\n",
      "Final Portfolio Value: 10000.00\n"
     ]
    }
   ],
   "source": [
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 3. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 4.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到, 引擎默认初始资金为10000.00；下一步尝试自定义初始资金为1000000.0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "Final Portfolio Value: 1000000.00\n"
     ]
    }
   ],
   "source": [
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 3. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 4. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 5.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始资金被成功修改为1000000.00。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1.2 尝试添加数据\n",
    "尝试执行完引擎后，下一步要做的就是尝试添加数据，和上一节相比，输出不会有任何变化，这是因为仅加载了数据，但并未使用它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "Final Portfolio Value: 1000000.00\n"
     ]
    }
   ],
   "source": [
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "import datetime\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 8, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 4. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 5. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 6.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.3 运行第一个策略\n",
    "设置了资金和行情数据后，就可以创建策略，训练模型。策略必须继承bt策略类，接下来执行一个例子，如果价格下降到2500点以下，就认为价格到达了低点，记录收盘的价格。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2020-03-12, Close, 2480.64\n",
      "2020-03-16, Close, 2386.13\n",
      "2020-03-18, Close, 2398.10\n",
      "2020-03-19, Close, 2409.39\n",
      "2020-03-20, Close, 2304.92\n",
      "2020-03-23, Close, 2237.40\n",
      "2020-03-24, Close, 2447.33\n",
      "2020-03-25, Close, 2475.56\n",
      "2020-04-01, Close, 2470.50\n",
      "2020-04-03, Close, 2488.65\n",
      "Final Portfolio Value: 1000000.00\n"
     ]
    }
   ],
   "source": [
    "# 3.1 创建的策略必须继承bt策略基类\n",
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    # 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易日的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 如果价格低于2500点，就记录下收盘价\n",
    "        if self.dataclose[0] < 2500:\n",
    "            self.log('Close, %.2f' % self.dataclose[0])\n",
    "\n",
    " # 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 8, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中\n",
    "    cerebro.addstrategy(TestStrategy)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "价格到达低点，触发了我们的条件，记录了收盘价，那干嘛不直接执行买入呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2020-03-12, BUY CREATE, 2480.64\n",
      "2020-03-16, BUY CREATE, 2386.13\n",
      "2020-03-18, BUY CREATE, 2398.10\n",
      "2020-03-19, BUY CREATE, 2409.39\n",
      "2020-03-20, BUY CREATE, 2304.92\n",
      "2020-03-23, BUY CREATE, 2237.40\n",
      "2020-03-24, BUY CREATE, 2447.33\n",
      "2020-03-25, BUY CREATE, 2475.56\n",
      "2020-04-01, BUY CREATE, 2470.50\n",
      "2020-04-03, BUY CREATE, 2488.65\n",
      "Final Portfolio Value: 1009296.40\n"
     ]
    }
   ],
   "source": [
    "# 3.1 创建的策略必须继承bt策略基类\n",
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    # 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根K线或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.5 0 当前交易节点的索引，-1 上一个交易节点的索引\n",
    "        if self.dataclose[0] < 2500:\n",
    "\n",
    "            # 3.6 条件满足,则发出下单指令并log记录，默认使用下一根K线的开盘价；如果未指定买入标的，则买入self.datas[0]的标的\n",
    "            self.buy()\n",
    "            self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    " # 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 8, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中\n",
    "    cerebro.addstrategy(TestStrategy)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的例子中，日志记录了触发点的日期，买入标识，以及收盘价。但我们不知道的是，发出的订单是否被执行了，如果执行了，是什么价格，买了多少，以及如何选择卖出时机呢？下面我们要获取订单的状态，以及寻找卖点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2020-03-13, BUY EXECUTED, 2569.99\n",
      "2020-05-28, SELL EXECUTED, 3046.61\n",
      "Final Portfolio Value: 1000476.62\n"
     ]
    }
   ],
   "source": [
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "        self.order = None\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    ## 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.4.1 为了简单示例，检查是否有处理中的订单， 不再重复下\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # 3.4.2 检测是否已经有持仓\n",
    "        if not self.position:\n",
    "\n",
    "            # 3.4.3 没有持仓，判断是否满足买的条件\n",
    "            if self.dataclose[0] < 2500:\n",
    "\n",
    "                # 3.4.4 价格低于2500，到达买点，下单，默认使用下一根K线的开盘价；\n",
    "                # self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.5 跟踪订单\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            # 3.4.6 已经有持仓，判断是否到达卖的条件\n",
    "            if self.dataclose[0] > 3000:\n",
    "                # 3.4.7 价格高于3000，市场被高估，卖出\n",
    "                # self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.8 跟踪订单\n",
    "                self.order = self.sell()\n",
    "\n",
    "    # 3.5 订单状态回调\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # 3.5.1 状态变更信息，直接返回\n",
    "            return\n",
    "\n",
    "        # 3.5.2 检查订单是否已完成，有可能回测拒单：因为剩余资金可能不足\n",
    "        if order.status in [order.Completed]:\n",
    "            # 3.5.3 订单已执行，无论买卖都打印\n",
    "            if order.isbuy(): \n",
    "                self.log('BUY EXECUTED, %.2f' % order.executed.price)\n",
    "            elif order.issell():\n",
    "                self.log('SELL EXECUTED, %.2f' % order.executed.price)\n",
    "            # 3.5.4 记录执行的日期\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            # 3.5.5 订单未能执行\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # 3.5.6 订单均已处理完成\n",
    "        self.order = None\n",
    "\n",
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 8, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中\n",
    "    cerebro.addstrategy(TestStrategy)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，订单的买入、卖出及执行价格都被记录了下来。在我们未指定下单数量的时候，系统默认为1（买入价格为下一个K线的开盘价）。系统最终盈利: 3046.61 - 2569.99 = 476.62。如果最后一天是有持仓的，则在计算最终组合价值时，使用持有股票最后的收盘价来进行计算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.4 利润的侵蚀者：佣金\n",
    "无论在什么时候，佣金都不可忽视。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2020-03-13, BUY EXECUTED, Price: 2569.99, Cost: 2569.99, Comm 0.77\n",
      "2020-05-28, SELL EXECUTED, Price: 3046.61, Cost: 2569.99, Comm 0.91\n",
      "2020-05-28, OPERATION PROFIT, GROSS 476.62, NET 474.94\n",
      "Final Portfolio Value: 1000474.94\n"
     ]
    }
   ],
   "source": [
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    ## 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.4.1 检查是否有处理中的订单， 不再重复下\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # 3.4.2 检测是否已经有持仓\n",
    "        if not self.position:\n",
    "\n",
    "            # 3.4.3 没有持仓，判断是否满足买的条件\n",
    "            if self.dataclose[0] < 2500:\n",
    "\n",
    "                # 3.4.4 条件满足，下单，默认使用下一根K线的开盘价；\n",
    "                # self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.5 跟踪订单\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            # 3.4.6 已经有持仓，判断是否到达卖的条件\n",
    "            if self.dataclose[0] > 3000:\n",
    "                # 3.4.7 条件满足，卖出\n",
    "                # self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.8 跟踪订单\n",
    "                self.order = self.sell()\n",
    "\n",
    "    # 3.5 订单状态回调\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # 3.5.1 状态变更信息，直接返回\n",
    "            return\n",
    "\n",
    "        # 3.5.2 检查订单是否已完成，有可能回测拒单：因为剩余资金可能不足\n",
    "        if order.status in [order.Completed]:\n",
    "            # 3.5.3 订单已执行，无论买卖都打印\n",
    "            if order.isbuy(): \n",
    "                self.log(\n",
    "                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                    (order.executed.price,\n",
    "                    order.executed.value,\n",
    "                    order.executed.comm))\n",
    "\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            elif order.issell():\n",
    "                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                        (order.executed.price,\n",
    "                        order.executed.value,\n",
    "                        order.executed.comm))\n",
    "            # 3.5.4 记录执行的日期\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            # 3.5.5 订单未能执行\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # 3.5.6 订单均已处理完成\n",
    "        self.order = None\n",
    "\n",
    "    # 3.6.1 交易结果回调\n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n",
    "                (trade.pnl, trade.pnlcomm))\n",
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 6, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中\n",
    "    cerebro.addstrategy(TestStrategy)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 4.1 佣金设置为万3\n",
    "    cerebro.broker.setcommission(commission=0.0003)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相比上一节，增加了佣金设置，即Comm, 双向收取，如果买卖频率过高，将极大的侵蚀利润。这里还没有添加印花税。476.62-0.77-0.91= 474.94, 474.94才是暂时属于我们的利润。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.5  自定义策略参数\n",
    "策略应该尽量是模板式的，这样环境变了才能更方便的修改优化，这也契合backtrader的设计思想。那怎么自定义策略参数呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2020-03-13, BUY EXECUTED, Price: 2569.99, Cost: 2569.99, Comm 0.77\n",
      "2020-06-09, SELL EXECUTED, Price: 3213.32, Cost: 2569.99, Comm 0.96\n",
      "2020-06-09, OPERATION PROFIT, GROSS 643.33, NET 641.60\n",
      "Final Portfolio Value: 1000641.60\n"
     ]
    }
   ],
   "source": [
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self, sellprice):\n",
    "        self.dataclose = self.datas[0].close\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "        self.sellprice = sellprice\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    ## 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.4.1 检查是否有处理中的订单， 不再重复下\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # 3.4.2 检测是否已经有持仓\n",
    "        if not self.position:\n",
    "\n",
    "            # 3.4.3 没有持仓，判断是否满足买的条件\n",
    "            if self.dataclose[0] < 2500:\n",
    "\n",
    "                # 3.4.4 条件满足，下单，默认使用下一根K线的开盘价；\n",
    "                # self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.5 跟踪订单\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            # 3.4.6 已经有持仓，判断是否到达卖的条件\n",
    "            if self.dataclose[0] > self.sellprice:\n",
    "                # 3.4.7 条件满足，卖出\n",
    "                # self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.8 跟踪订单\n",
    "                self.order = self.sell()\n",
    "\n",
    "    # 3.5 订单状态回调\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # 3.5.1 状态变更信息，直接返回\n",
    "            return\n",
    "\n",
    "        # 3.5.2 检查订单是否已完成，有可能回测拒单：因为剩余资金可能不足\n",
    "        if order.status in [order.Completed]:\n",
    "            # 3.5.3 订单已执行，无论买卖都打印\n",
    "            if order.isbuy(): \n",
    "                self.log(\n",
    "                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                    (order.executed.price,\n",
    "                    order.executed.value,\n",
    "                    order.executed.comm))\n",
    "\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            elif order.issell():\n",
    "                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                        (order.executed.price,\n",
    "                        order.executed.value,\n",
    "                        order.executed.comm))\n",
    "            # 3.5.4 记录执行的日期\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            # 3.5.5 订单未能执行\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # 3.5.6 订单均已处理完成\n",
    "        self.order = None\n",
    "\n",
    "    # 3.6.1 交易结果回调\n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n",
    "                (trade.pnl, trade.pnlcomm))\n",
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 6, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中,自定义卖出参数，到3200才卖\n",
    "    cerebro.addstrategy(TestStrategy, sellprice=3200)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 4.1 佣金设置为万3\n",
    "    cerebro.broker.setcommission(commission=0.0003)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里仅简单的自定义了卖出价格，随着模型的逐步建立，个性化的参数也会不断增加，最好把参数保存到文件或者缓存redis集群中。\n",
    "\n",
    "#### 1.1.6 添加技术指标\n",
    "除了价格信息，很多时候技术指标更能反应当前的行情趋势，而无需关心现在价位，因为无论价格多高，只要还会上涨，就可以通过做多盈利。幸运的是，backtrader内置了常用的金融分析库talib，可以很方便的添加技术指标；这里需要提前说明的是，talib中的部分技术指标计算与中国国内券商采用的部分指标计算方式略有不同，这点在后面会进行说明。这里构造一个双均线策略，即MA5上穿MA15则买入，MA5下穿MA15则卖出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2019-10-15, BUY CREATE, 2995.68\n",
      "2019-10-16, BUY EXECUTED, Price: 2989.68, Cost: 2989.68, Comm 0.90\n",
      "2019-12-06, SELL EXECUTED, Price: 3134.62, Cost: 2989.68, Comm 0.94\n",
      "2019-12-06, OPERATION PROFIT, GROSS 144.94, NET 143.10\n",
      "2019-12-10, BUY CREATE, 3132.52\n",
      "2019-12-11, BUY EXECUTED, Price: 3135.75, Cost: 3135.75, Comm 0.94\n",
      "2020-01-30, SELL EXECUTED, Price: 3256.45, Cost: 3135.75, Comm 0.98\n",
      "2020-01-30, OPERATION PROFIT, GROSS 120.70, NET 118.78\n",
      "2020-02-07, BUY CREATE, 3327.71\n",
      "2020-02-10, BUY EXECUTED, Price: 3318.28, Cost: 3318.28, Comm 1.00\n",
      "2020-02-25, SELL EXECUTED, Price: 3238.94, Cost: 3318.28, Comm 0.97\n",
      "2020-02-25, OPERATION PROFIT, GROSS -79.34, NET -81.31\n",
      "2020-03-30, BUY CREATE, 2626.65\n",
      "2020-03-31, BUY EXECUTED, Price: 2614.69, Cost: 2614.69, Comm 0.78\n",
      "2020-05-18, SELL EXECUTED, Price: 2913.86, Cost: 2614.69, Comm 0.87\n",
      "2020-05-18, OPERATION PROFIT, GROSS 299.17, NET 297.51\n",
      "2020-05-20, BUY CREATE, 2971.61\n",
      "2020-05-21, BUY EXECUTED, Price: 2969.95, Cost: 2969.95, Comm 0.89\n",
      "2020-06-17, SELL EXECUTED, Price: 3136.13, Cost: 2969.95, Comm 0.94\n",
      "2020-06-17, OPERATION PROFIT, GROSS 166.18, NET 164.35\n",
      "Final Portfolio Value: 1000642.44\n"
     ]
    }
   ],
   "source": [
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "        # 3.2.1 数据初始化的时候就添加技术指标的计算\n",
    "        self.sma5 = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=5)\n",
    "        self.sma15 = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=15)\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    ## 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.4.1 检查是否有处理中的订单， 不再重复下\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # 3.4.2 检测是否已经有持仓\n",
    "        if not self.position:\n",
    "\n",
    "            # 3.4.3 没有持仓，判断是否满足买的条件\n",
    "            if self.sma5[-1] < self.sma15[-1] and self.sma5[0] > self.sma15[0]:\n",
    "\n",
    "                # 3.4.4 前一天条件满足，下单，默认使用下一根K线的开盘价；\n",
    "                self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.5 跟踪订单\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            # 3.4.6 已经有持仓，判断是否到达卖的条件\n",
    "            if self.sma5[-1] > self.sma15[-1] and self.sma5[0] < self.sma15[0]:\n",
    "                # 3.4.7 条件满足，卖出\n",
    "                # self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.8 跟踪订单\n",
    "                self.order = self.sell()\n",
    "\n",
    "    # 3.5 订单状态回调\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # 3.5.1 状态变更信息，直接返回\n",
    "            return\n",
    "\n",
    "        # 3.5.2 检查订单是否已完成，有可能回测拒单：因为剩余资金可能不足\n",
    "        if order.status in [order.Completed]:\n",
    "            # 3.5.3 订单已执行，无论买卖都打印\n",
    "            if order.isbuy(): \n",
    "                self.log(\n",
    "                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                    (order.executed.price,\n",
    "                    order.executed.value,\n",
    "                    order.executed.comm))\n",
    "\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            elif order.issell():\n",
    "                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                        (order.executed.price,\n",
    "                        order.executed.value,\n",
    "                        order.executed.comm))\n",
    "            # 3.5.4 记录执行的日期\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            # 3.5.5 订单未能执行\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # 3.5.6 订单均已处理完成\n",
    "        self.order = None\n",
    "\n",
    "    # 3.6.1 交易结果回调\n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n",
    "                (trade.pnl, trade.pnlcomm))\n",
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 6, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中\n",
    "    cerebro.addstrategy(TestStrategy)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 4.1 佣金设置为万3\n",
    "    cerebro.broker.setcommission(commission=0.0003)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.7 可视化买卖点\n",
    "尽管已经执行了策略，但结果却不是那么显而易见，在哪个点买入，哪个点卖出，是否符合逻辑，当时的趋势怎么样,，即使日志能找到也很抽象，显然不符合人来的视觉习惯，怎么可以图形化展示呢？backtrader依赖matplotlib提供了将执行结果可视化的功能，这里继续使用上一节的例子，对结果添加可视化，代码里唯一的改动是添加了第八项绘图展示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2019-10-15, BUY CREATE, 2995.68\n",
      "2019-10-16, BUY EXECUTED, Price: 2989.68, Cost: 2989.68, Comm 0.90\n",
      "2019-12-06, SELL EXECUTED, Price: 3134.62, Cost: 2989.68, Comm 0.94\n",
      "2019-12-06, OPERATION PROFIT, GROSS 144.94, NET 143.10\n",
      "2019-12-10, BUY CREATE, 3132.52\n",
      "2019-12-11, BUY EXECUTED, Price: 3135.75, Cost: 3135.75, Comm 0.94\n",
      "2020-01-30, SELL EXECUTED, Price: 3256.45, Cost: 3135.75, Comm 0.98\n",
      "2020-01-30, OPERATION PROFIT, GROSS 120.70, NET 118.78\n",
      "2020-02-07, BUY CREATE, 3327.71\n",
      "2020-02-10, BUY EXECUTED, Price: 3318.28, Cost: 3318.28, Comm 1.00\n",
      "2020-02-25, SELL EXECUTED, Price: 3238.94, Cost: 3318.28, Comm 0.97\n",
      "2020-02-25, OPERATION PROFIT, GROSS -79.34, NET -81.31\n",
      "2020-03-30, BUY CREATE, 2626.65\n",
      "2020-03-31, BUY EXECUTED, Price: 2614.69, Cost: 2614.69, Comm 0.78\n",
      "2020-05-18, SELL EXECUTED, Price: 2913.86, Cost: 2614.69, Comm 0.87\n",
      "2020-05-18, OPERATION PROFIT, GROSS 299.17, NET 297.51\n",
      "2020-05-20, BUY CREATE, 2971.61\n",
      "2020-05-21, BUY EXECUTED, Price: 2969.95, Cost: 2969.95, Comm 0.89\n",
      "2020-06-17, SELL EXECUTED, Price: 3136.13, Cost: 2969.95, Comm 0.94\n",
      "2020-06-17, OPERATION PROFIT, GROSS 166.18, NET 164.35\n",
      "Final Portfolio Value: 1000642.44\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TestStrategy(bt.Strategy):\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "        # 3.2.1 数据初始化的时候就添加技术指标的计算\n",
    "        self.sma5 = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=5)\n",
    "        self.sma15 = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=15)\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    ## 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.4.1 检查是否有处理中的订单， 不再重复下\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # 3.4.2 检测是否已经有持仓\n",
    "        if not self.position:\n",
    "\n",
    "            # 3.4.3 没有持仓，判断是否满足买的条件\n",
    "            if self.sma5[-1] < self.sma15[-1] and self.sma5[0] > self.sma15[0]:\n",
    "\n",
    "                # 3.4.4 前一天条件满足，下单，默认使用下一根K线的开盘价；\n",
    "                self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.5 跟踪订单\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            # 3.4.6 已经有持仓，判断是否到达卖的条件\n",
    "            if self.sma5[-1] > self.sma15[-1] and self.sma5[0] < self.sma15[0]:\n",
    "                # 3.4.7 条件满足，卖出\n",
    "                # self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.8 跟踪订单\n",
    "                self.order = self.sell()\n",
    "\n",
    "    # 3.5 订单状态回调\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # 3.5.1 状态变更信息，直接返回\n",
    "            return\n",
    "\n",
    "        # 3.5.2 检查订单是否已完成，有可能回测拒单：因为剩余资金可能不足\n",
    "        if order.status in [order.Completed]:\n",
    "            # 3.5.3 订单已执行，无论买卖都打印\n",
    "            if order.isbuy(): \n",
    "                self.log(\n",
    "                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                    (order.executed.price,\n",
    "                    order.executed.value,\n",
    "                    order.executed.comm))\n",
    "\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            elif order.issell():\n",
    "                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                        (order.executed.price,\n",
    "                        order.executed.value,\n",
    "                        order.executed.comm))\n",
    "            # 3.5.4 记录执行的日期\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            # 3.5.5 订单未能执行\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # 3.5.6 订单均已处理完成\n",
    "        self.order = None\n",
    "\n",
    "    # 3.6.1 交易结果回调\n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n",
    "                (trade.pnl, trade.pnlcomm))\n",
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 6, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 添加策略类到引擎中\n",
    "    cerebro.addstrategy(TestStrategy)\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 4.1 佣金设置为万3\n",
    "    cerebro.broker.setcommission(commission=0.0003)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()\n",
    "    # 7.打印策略执行后的资金\n",
    "    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 8. 调用plot()将结果绘图展示\n",
    "    cerebro.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.8 优化策略参数\n",
    "在双均线的例子中，我们使用的分别是5日和15日K线。不同的标的对应的参与人群、股本、行业景气度等不同，导致每个标的可能都有适合自己的参数，人工输入校验找规律对比显然是不现实的，那使用backtrader如何实现呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n",
      "2020-06-19, (shortMaPeriod  5) (longMaPeriod 20) Ending Value 1000577.52\n",
      "2020-06-19, (shortMaPeriod  5) (longMaPeriod 60) Ending Value 1000227.79\n",
      "2020-06-19, (shortMaPeriod  5) (longMaPeriod 15) Ending Value 1000642.44\n",
      "2020-06-19, (shortMaPeriod  5) (longMaPeriod 30) Ending Value 1000340.50\n",
      "2020-06-19, (shortMaPeriod 10) (longMaPeriod 20) Ending Value 1000229.32\n",
      "2020-06-19, (shortMaPeriod 10) (longMaPeriod 15) Ending Value 1000433.96\n",
      "2020-06-19, (shortMaPeriod 10) (longMaPeriod 60) Ending Value 1000213.74\n",
      "2020-06-19, (shortMaPeriod 10) (longMaPeriod 30) Ending Value 1000222.38\n"
     ]
    }
   ],
   "source": [
    "class TestStrategy(bt.Strategy):\n",
    "    params = (\n",
    "        ('shortMaPeriod', 5),\n",
    "        ('longMaPeriod', 15),\n",
    "    )\n",
    "    # 3.2 数据和策略添加到同一引擎后，在策略中可通过datas访问数据，这里仅获取了传送数据的收盘价信息\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "        # 3.2.1 数据初始化的时候就添加技术指标的计算\n",
    "        self.smashort = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=self.params.shortMaPeriod)\n",
    "        self.smalong = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=self.params.longMaPeriod)\n",
    "\n",
    "    # 3.3 定义本策略的日志文件\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    ## 3.4 添加策略逻辑的地方，添加数据后，遍历每个日期进行调用，当前在的时间点坐标为0，其前面一根或者说之前一个交易节点的索引为-1，依次类推\n",
    "    def next(self):\n",
    "        # 3.4.1 检查是否有处理中的订单， 不再重复下\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # 3.4.2 检测是否已经有持仓\n",
    "        if not self.position:\n",
    "\n",
    "            # 3.4.3 没有持仓，判断是否满足买的条件\n",
    "            if self.smashort[-1] < self.smalong[-1] and self.smashort[0] > self.smalong[0]:\n",
    "\n",
    "                # 3.4.4 前一天条件满足，下单，默认使用下一根K线的开盘价；\n",
    "                # self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.5 跟踪订单\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            # 3.4.6 已经有持仓，判断是否到达卖的条件\n",
    "            if self.smashort[-1] > self.smalong[-1] and self.smashort[0] < self.smalong[0]:\n",
    "                # 3.4.7 条件满足，卖出\n",
    "                # self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # 3.4.8 跟踪订单\n",
    "                self.order = self.sell()\n",
    "\n",
    "    # 3.5 订单状态回调\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # 3.5.1 状态变更信息，直接返回\n",
    "            return\n",
    "\n",
    "        # 3.5.2 检查订单是否已完成，有可能回测拒单：因为剩余资金可能不足\n",
    "        if order.status in [order.Completed]:\n",
    "            # 3.5.3 订单已执行，无论买卖都打印\n",
    "            if order.isbuy(): \n",
    "\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            \n",
    "            # 3.5.4 记录执行的日期\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            # 3.5.5 订单未能执行\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # 3.5.6 订单均已处理完成\n",
    "        self.order = None\n",
    "\n",
    "    # 3.6 交易结果回调\n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "    # 3.7 添加策略停止时的价值统计\n",
    "    def stop(self):\n",
    "        self.log('(shortMaPeriod %2d) (longMaPeriod %2d) Ending Value %.2f' %\n",
    "                 (self.params.shortMaPeriod, self.params.longMaPeriod, self.broker.getvalue()))\n",
    "        \n",
    "# 引用backtrader\n",
    "import backtrader as bt\n",
    "if __name__ == '__main__':\n",
    "    # 1. 初始化引擎\n",
    "    cerebro = bt.Cerebro()\n",
    "    # 2.  引用一个数据源, 读取雅虎数据格式的本地文件\n",
    "    # 这里暂时不用关心数据的具体格式，自定义数据加载及其它源加载，后续章节会讲到\n",
    "    data = bt.feeds.YahooFinanceCSVData(\n",
    "    dataname='./GSPC.csv',\n",
    "    fromdate=datetime.datetime(2019, 8, 20),\n",
    "    todate=datetime.datetime(2020, 6, 20),\n",
    "    reverse=False)\n",
    "\n",
    "    # 添加数据到引擎中\n",
    "    cerebro.adddata(data)\n",
    "    # 3. 参数优化调用optstrategy,设置传入参数范围\n",
    "    cerebro.optstrategy(TestStrategy, shortMaPeriod=range(5,15, 5), longMaPeriod=[15, 20, 30,60])\n",
    "    # 4.设置初始化资金为100万\n",
    "    cerebro.broker.setcash(1000000.0)\n",
    "    # 4.1 佣金设置为万3\n",
    "    cerebro.broker.setcommission(commission=0.0003)\n",
    "    # 5. 打印策略执行前的资金\n",
    "    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n",
    "    # 6. 执行引擎\n",
    "    cerebro.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "短均线使用了5\\10两种均线类型,长均线使用15\\20\\30\\60四种均线类型，共产生2*4 = 8种结果，可以看到，不同参数的回测结果是不一样的，其短均线为5，长均线为15时获取到了最大收益。\n"
   ]
  }
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